Active Inference for an Intelligent Agent in Autonomous Reconnaissance Missions
- URL: http://arxiv.org/abs/2510.17450v1
- Date: Mon, 20 Oct 2025 11:35:46 GMT
- Title: Active Inference for an Intelligent Agent in Autonomous Reconnaissance Missions
- Authors: Johan Schubert, Farzad Kamrani, Tove Gustavi,
- Abstract summary: We develop an active inference route-planning method for autonomous control of intelligent agents.<n>The aim is to reconnoiter a geographical area to maintain a common operational picture.
- Score: 0.764671395172401
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We develop an active inference route-planning method for the autonomous control of intelligent agents. The aim is to reconnoiter a geographical area to maintain a common operational picture. To achieve this, we construct an evidence map that reflects our current understanding of the situation, incorporating both positive and "negative" sensor observations of possible target objects collected over time, and diffusing the evidence across the map as time progresses. The generative model of active inference uses Dempster-Shafer theory and a Gaussian sensor model, which provides input to the agent. The generative process employs a Bayesian approach to update a posterior probability distribution. We calculate the variational free energy for all positions within the area by assessing the divergence between a pignistic probability distribution of the evidence map and a posterior probability distribution of a target object based on the observations, including the level of surprise associated with receiving new observations. Using the free energy, we direct the agents' movements in a simulation by taking an incremental step toward a position that minimizes the free energy. This approach addresses the challenge of exploration and exploitation, allowing agents to balance searching extensive areas of the geographical map while tracking identified target objects.
Related papers
- Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration? [83.13508919229939]
Theory of Space is defined as an agent's ability to actively acquire information through self-directed, active exploration.<n>A key innovation is spatial belief probing, which prompts models to reveal their internal spatial representations at each step.<n>Our findings suggest that current foundation models struggle to maintain coherent, revisable spatial beliefs during active exploration.
arXiv Detail & Related papers (2026-02-04T19:06:40Z) - Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer [15.605693371392212]
This work proposes a novel MADRL-based method for target localization in uncertain environments.<n>The observations of the agents are designed in an optimized manner to capture essential information in the environment.<n>A Deep Learning model builds on the knowledge from the MADRL model to accurately estimating the target location if it is unreachable.
arXiv Detail & Related papers (2025-01-19T02:58:22Z) - Diffusion as Reasoning: Enhancing Object Navigation via Diffusion Model Conditioned on LLM-based Object-Room Knowledge [9.465351278799016]
We propose a novel approach to enhancing the ObjectNav task.<n>We train a diffusion model to learn the statistical distribution patterns of objects in semantic maps.<n>Using the map of the explored regions during navigation as the condition to generate the map of the unknown regions, we realize the long-term goal reasoning of the target object.
arXiv Detail & Related papers (2024-10-29T08:10:06Z) - Diffusion Models for Multi-target Adversarial Tracking [0.49157446832511503]
Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction.
As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety.
This paper presents Constrained Agent-based Diffusion for Enhanced Multi-Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations.
arXiv Detail & Related papers (2023-07-12T15:34:39Z) - Occupancy Flow Fields for Motion Forecasting in Autonomous Driving [36.64394937525725]
We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents.
Our representation is a-temporal grid with each grid cell containing both the probability magnitude of the cell being occupied by any agent, and a two-dimensional flow vector representing the direction of the motion in that cell.
We report experimental results on a large in-house autonomous driving dataset and the INTERACTION dataset, and show that our model outperforms state-of-the-art models.
arXiv Detail & Related papers (2022-03-08T06:26:50Z) - Trajectory Forecasting from Detection with Uncertainty-Aware Motion
Encoding [121.66374635092097]
Trajectories obtained from object detection and tracking are inevitably noisy.
We propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories.
arXiv Detail & Related papers (2022-02-03T09:09:56Z) - Information is Power: Intrinsic Control via Information Capture [110.3143711650806]
We argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model.
This objective induces an agent to both gather information about its environment, corresponding to reducing uncertainty, and to gain control over its environment, corresponding to reducing the unpredictability of future world states.
arXiv Detail & Related papers (2021-12-07T18:50:42Z) - Deceptive Decision-Making Under Uncertainty [25.197098169762356]
We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks.
By modeling the agent's behavior as a Markov decision process, we consider a setting where the agent aims to reach one of multiple potential goals.
We propose a novel approach to model observer predictions based on the principle of maximum entropy and to efficiently generate deceptive strategies.
arXiv Detail & Related papers (2021-09-14T14:56:23Z) - Adversarial Intrinsic Motivation for Reinforcement Learning [60.322878138199364]
We investigate whether the Wasserstein-1 distance between a policy's state visitation distribution and a target distribution can be utilized effectively for reinforcement learning tasks.
Our approach, termed Adversarial Intrinsic Motivation (AIM), estimates this Wasserstein-1 distance through its dual objective and uses it to compute a supplemental reward function.
arXiv Detail & Related papers (2021-05-27T17:51:34Z) - Modulation of viability signals for self-regulatory control [1.370633147306388]
We revisit the role of instrumental value as a driver of adaptive behavior.
For reinforcement learning tasks, the distribution of preferences replaces the notion of reward.
arXiv Detail & Related papers (2020-07-18T01:11:51Z) - Traffic Agent Trajectory Prediction Using Social Convolution and
Attention Mechanism [57.68557165836806]
We propose a model to predict the trajectories of target agents around an autonomous vehicle.
We encode the target agent history trajectories as an attention mask and construct a social map to encode the interactive relationship between the target agent and its surrounding agents.
To verify the effectiveness of our method, we widely compare with several methods on a public dataset, achieving a 20% error decrease.
arXiv Detail & Related papers (2020-07-06T03:48:08Z) - Maximizing Information Gain in Partially Observable Environments via
Prediction Reward [64.24528565312463]
This paper tackles the challenge of using belief-based rewards for a deep RL agent.
We derive the exact error between negative entropy and the expected prediction reward.
This insight provides theoretical motivation for several fields using prediction rewards.
arXiv Detail & Related papers (2020-05-11T08:13:49Z) - InfoBot: Transfer and Exploration via the Information Bottleneck [105.28380750802019]
A central challenge in reinforcement learning is discovering effective policies for tasks where rewards are sparsely distributed.
We propose to learn about decision states from prior experience.
We find that this simple mechanism effectively identifies decision states, even in partially observed settings.
arXiv Detail & Related papers (2019-01-30T15:33:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.